Ereifej, Deena
(2024)
Identifying Risk Factors for Clostridioides difficile Acquisition from Transmission in Acute-Care Hospitals.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Background:
Healthcare-associated infections develop during or soon after receiving healthcare services or being in a healthcare setting. Clostridioides difficile is a common and preventable healthcare-associated infection. Whole genome sequencing (WGS) can improve differentiation of C. difficile carriage by identifying genetically related isolates acquired from hospital transmission. Risk factors for healthcare-associated C. difficile infections (CDIs) have been identified, but no published study has identified epidemiological factors characterizing risk of transmission in healthcare settings. Identifying risk factors for C. difficile acquisition from transmission can help guide infection prevention interventions to reduce rates of transmission.
Objectives/Aims:
The goal of this study was to identify risk factors for infection with hospital-acquired C. difficile compared to patients with no genetically similar in-hospital patient source.
Method(s) Used/Approach Taken:
Data was collected from the study hospital’s electronic health record (EHR) for all patients with healthcare-associated CDIs during the study period. Cases were defined as patients with CDI whose isolate clustered with another genetically similar isolate, excluding index patients. Controls were patients with CDI whose isolates did not cluster or were an index patient. A prediction model was generated for in-hospital C. difficile acquisition using EHR data. Elastic net regression (ENR) was utilized with ten-fold cross validation to select significant risk factors. All pairwise interactions were formulated and tested for association using ENR. A prediction model was determined using the selected risk factors in a multivariate logistic regression analysis.
Results:
Among 809 patients with healthcare-associated C. difficile, 114 were excluded from the analysis and the study cohort contained 84 cases and 611 controls. The prediction model identified risk factors (transplant procedure, length of stay, antibiotic receipt and a virulence factor gene) and protective factors (autoimmune disorder, ICU admission and virulence factor genes). The fraction of variance explained by this model for predicting C. difficile acquisition was 28.5%. Eight variables predicted >90% of the model variance.
Summary/Conclusions:
Risk factors for C. difficile transmission can help guide infection preventionists in mitigating transmission of C. difficile by implementing targeted interventions and protocols. These would include pre-emptive contact precautions when handling high-risk patients, enhanced environmental cleaning or asymptomatic C. difficile screening.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
2 January 2024 |
| Date Type: |
Publication |
| Defense Date: |
5 December 2023 |
| Approval Date: |
2 January 2024 |
| Submission Date: |
12 December 2023 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
54 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Infectious Diseases and Microbiology |
| Degree: |
MPH - Master of Public Health |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Clostridiodes difficile, infection prevention, risk factors, prediction model, elastic net regression |
| Date Deposited: |
02 Jan 2024 22:28 |
| Last Modified: |
02 Jan 2024 22:28 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/45615 |
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